A visual information source requires a transmission or a storage medium to convey its message to the observer. The fidelity of transmission and reproduction of the message is closely related to and dependent on the available medium capacity and the manner in which it is used. In the digital world the medium capacity is expressed in bits per second or the bit rate. The transmission of visual information can be improved by compressing the video signal and transmitting the compressed signal. The goal of digital video compression is to represent an image with as low a bit rate as possible, while preserving an appropriate level of picture quality for a given application. Compression is achieved by identifying and removing redundancies.
A bit rate reduction system operates by removing redundant information from the signal at the encoder prior to transmission and re-inserting it at the decoder. An encoder and decoder pair are referred to as a ‘codec’. In video signals, two distinct kinds of redundancy can be identified.                i. Spatial and temporal redundancy where pixel values are not independent, but are correlated with their neighbors both within the same frame and across frames. To some extent, the value of a pixel is predictable given the values of neighboring pixels.        ii. Psycho-visual redundancy where the human eye has a limited response to fine spatial detail and is less sensitive to detail near object edges or around shot-changes. Consequently, controlled impairments introduced into the decoded picture by the bit rate reduction process are not visible to a human observer.        
At its most basic level, compression is performed when an input video stream is analyzed and information that is indiscernible to the viewer is discarded. Each event is then assigned a code where commonly occurring events are assigned fewer bits and rare events are assigned more bits. These steps are commonly referred to as signal analysis, quantization and variable length encoding. Common methods for compression include discrete cosine transform (DCT), vector quantization (VQ), fractal compression, and discrete wavelet transform (DWT).
Most recorded media content tends to contain some noise. Noise is mostly random, unwanted and spurious variation in a signal. Common sources of noise are electrical and electromagnetic signals external to the recorded media. Noise does not compress well and most video compression systems utilize additional storage space and computational resources to reduce the noise level present in the signal. Noise reduction and filtering can substantially improve the video quality received by the viewer if the right techniques are applied to remove noise. Selectively removing noise is a challenge because noise shares the same space as valuable picture data. An ideal noise reduction process will allow powerful suppression of random noise while preserving clean video content. Good noise reduction means applying filters that preserve details such as edge structure in an image while avoiding blurring, trailing or other effects adverse to the fidelity of the image. Most filtering algorithms such as Motion Compensated Temporal Filtering (MCTF) add a heavy pre-filtering computational load on the encoder. This is a common problem with most temporal processors. What is needed is a new method to efficiently reduce noise while encoding a video stream.